What are common scenarios where clustered indexes are preferred over non-clustered indexes in SQL?

To explain an Index, the simplest analogy that comes to my mind is a phone book dictionary or any book with some sort of appendix at the end or at the beginning of the book. The phone dictionary is sorted by specific order and by looking into the dictionary-index first we can quickly find on what page specific content is located. The Index is a similar structure on the table. It is a set of one or more columns stored in a specific order. It also has information where the remaining columns data for the same row is located. Depending on the underlying db (SQL, no-sql, columnar) technology there are few variations how the index is physically stored and how to lookup/pointer logic is implemented to retrieve the rest of the data.

The explanation of how an index stores a subset of the columns and rows of a table in a specific order is accurate and easy to understand. By using an index, database engines can find the data more quickly and efficiently, which is crucial for optimizing query performance. Additionally, the mention that multiple indexes can be created on a table, depending on the queries you want to optimize, highlights the flexibility of indexes and how they can be tailored to specific query patterns. Overall, your response provides a solid understanding of what an index is and why it's important in databases.

An index is a data structure that allows for fast and efficient data retrieval by providing a pointers for a data block based on specific search criteria. In an index database, the index block is different and data block is different. The database software uses the index to quickly locate and retrieve the required data with these pointers. This approach can greatly improve the performance of database searches, especially in cases where there is a large amount of data to be searched and data retrieval is smaller subset of data. Typically these are in OLTP environment.

I agree that an index is a pretty efficient data structure to find data - but note that in some databases the data is stored in the index. So it’s not just a pointer to the data but the data is there. One key point is that the data in an index needs to be organized by a “key”: the key determines how the data is sorted and later searched efficiently. As such, it should be an important item or items in the data Tuple.

Clustered indexes physically sort and order the data in a table according to the values of the columns in the table. A clustered index determines the physical order of the data in a table, and it can only be used on one table. By following the physical order of the index when a query is executed that uses the clustered index key in its search criteria, the database engine can quickly locate the required data. Clustered indexes are often used in tables where the data is frequently accessed in a range of values or in a sorted order, such as in a transactional or analytical database. They can improve query performance by minimizing the number of data pages that need to be accessed to retrieve the desired data.

Your clustered index should be sorted on the column(s) that are used by the real time analytical queries in there joins to the table in question. Don't wast this index on a query that access a few 10s of rows. This is for the queries that access a few thousand rows and roll up the results. You are avoiding the RID lookup, use it wisely.

Every non-clustered index takes up its own storage which scales with the size of the table. Each non-clustered index also adds additional overhead to the table's writes since each index needs to be updated as part of the same transaction when the underlying data changes. Don't go overboard with non-clustered indexes as you may start seeing a decrease in database performance.

A non-clustered index is an index type in a relational database that offers an alternative method for accessing data in a table. Unlike a clustered index, it does not modify the physical order of the data in the table. Instead, it generates a separate data structure that includes a sorted version of the indexed columns along with a pointer to the matching table row.

The excessive use of indexes, regardless of their type, negatively impacts the performance of a database. This is due to the additional overhead associated with maintenance, storage, and updates. In modeling data, consider specific use cases for both clustered and non-clustered indexes, and only select fields that are relevant to commonly executed queries. This is a continuous process that involves monitoring and refinement based on actual workload and statistics. It's worth noting that as data size increases, use of clustered indexes will require more disk space and memory than non-clustered indexes proportionally.

Choosing between clustered and non-clustered indexes depends on various factors, such as the table size, the number of queries performed, the type of queries executed, and the frequency of data modifications.

Generally you don't want to use only a datetime field for the clustered index. This is because you want the key to be as unique as possible and unchanging for maximal performance, and you can't always guarantee that there won't be 2 records with the same date stamp (putting aside capabilities of certain SQL software). Using an auto-incrementing id field is more preferable, but if you absolutely have to use a datetime field, consider using a Composite Key for the index.

Your description of scenarios where clustered indexes are preferred over non-clustered indexes in SQL is accurate and well-explained. The examples you provided are common use cases where the physical ordering of the data in the clustered index can greatly improve query performance. Additionally, you highlighted how the choice between clustered and non-clustered indexes depends on the specific query patterns and usage scenarios of the database, which is an important consideration when optimizing the performance of a database. Overall, your response provides a clear and concise explanation of when to use clustered indexes in SQL.

Clustered indexes are preferred over non-clustered indexes in SQL when it has Range queries, sorting, data volumes, insert methods.

A few hard earned by experience tips: 1.) ALWAYS have an INT date field available (e.g. instead of March 7, 2023 (datetime) use 20230307 (int)… it is sortable quickly, intuitive and way easier do deal with particularly in query joins. 2.) Do at least a non-clustered index of this data INT field and consider it being a composite with another field (e.g. Order Number). If unique as composite consider it as possible primary key instead of normal incremental field... depends partly if this is a transactional or a warehouse database. 3.) For Big Data e.g. in Hadoop environment always use partitions and the INT data field works well for this... this makes restaging logical and easier.

Indexing can greatly improve the read performance depending on the use case however it comes with its own challenges such as a little slower writes and maintainability challenges as the size of the database grows over time. A careful and thorough planning should be done before adding new indexes to existing tables with secondary indexes.